Are GPUs Essentially a Subscription?

Are graphics processing units more akin to a subscription than “capital investment?” 

Think about your own smartphone purchases. Yes, it is a hardware purchase. But it is also a hardware purchase with a relatively-short useful life. You plan to replace the device regularly. 

So data center shells are one thing, GPUs and other accelerators possibly quite another. The shell might be depreciated over 15 to 25 years. Processors might be depreciated over six years. 

So processors are akin to a subscription: they are capital, but also capital that must regularly be replaced. 

In other words, processors are formally capex, but also resemble operating expense. So unless you believe artificial intelligence essentially is a fad, the continuing demand for processors is at least the size of the current and projected installed base. 

That assumes, of course, that revenue earned by using all that infrastructure produces a profit. 

So a GPU cluster's economics are a race between two curves:

• The depreciation curve (how fast the asset's value erodes)

• The monetization curve (how fast the cluster recovers revenue against its capex).

The relevance for current debates about chip infrastructure are only partly about hyperscaler investment levels (whether they are overestimating demand). 

If demand exists, then processor capex is essentially a recurring function, and hence similar to a subscription. 

Assuming there is demand, infra outlays then have to be compared to monetization curves. 

If the latter grows faster than the former, there is no real problem. And there lies the friction and uncertainty.

Nvidia's shift to an annual product release schedule creates a two-year to three-year frontier processor obsolescence.

Where Hopper (2022), Blackwell (2024) and Rubin (2026) releases happened on a two-year schedule, Rubin Ultra (2027) is headed for a annual cycle.

Some will argue that Blackwell's efficiency gains over Hopper are large enough that older hardware becomes non-competitive for frontier training within 18 months to 36 months. 

But data center depreciation schedules for such gear now sit at six years. 

That gap between "accounting life" and "economic life" is at the heart of skeptical views on AI capex. 

The monetization picture also is dynamic. 

Per-unit prices are collapsing fast, as inference costs have dropped roughly 1,000 times in three years, with GPT-4-equivalent performance costing about $0.40 per million tokens in 2026 versus $20 in late 2022.

Goldman Sachs researchers project total token consumption growing 24 times between 2026 and 2030, so volume growth offsets price decay.

Total inference spending grew 320 percent even as per-token costs fell roughly 280-fold.

So the real question for any given cluster isn't "is the accounting depreciation schedule realistic" — it's whether cumulative revenue recovery clears the capex bar before the hardware's real economic obsolescence catches up to it. 

Is it "Different This Time?"

If you worked at any venture-capital-funded startup during the dot-com bubble, you might recall hearing one of the most-dangerous phrases in equity markets: “it’s different this time.

Maybe you recall being told “you don’t get it,” or any of the variants of the idea that traditional valuation metrics no longer apply:

• “it’s a new era” 

• “the old rules no longer apply”

• “valuations don’t matter.” 

In fact, there were all sorts of phrases suggesting old investment rules were essentially useless:

• "It’s a New Economy" (The claim was that the internet had fundamentally changed how economic value gets created, so old metrics didn't capture it anymore)

• "Get big fast"/ "Get large or get lost" (market share and growth mattered more than profit)

• "Eyeballs" and "eyeballs over earnings" (attention and traffic became a proxy for value)

• "First-mover advantage" (burning cash to grab a market before anyone else, on the theory that being first was worth more than being profitable)

• "Clicks, not bricks" (dismissing physical/traditional retail as legacy infrastructure that the internet would simply route around)

• "Old economy" (anything industrial, physical, or profit-focused got dismissed as backward-looking)

• "Network effects" (often invoked loosely to claim that a company's value would compound in ways traditional accounting couldn't measure)

• "Burn rate is a feature, not a bug" (the idea that losing money fast was actually evidence of aggressive growth, not weakness)

• "P/E ratios don't matter anymore" (only "eyeballs" or "mindshare"). 

One hears those things in asset bubbles. We heard it quite a lot during the dot-com or internet bubble. 

Era / Bubble	Example of “This Time Is Different” Reasoning	Outcome / Context	Source Link
Tulip Mania (Netherlands, 1630s)	Speculators treated rare tulip bulbs as a new, superior form of wealth whose prices could only rise; traditional notions of intrinsic value were set aside.	Prices collapsed ~99% in 1637.	NST article on historical examples
South Sea Bubble (UK, 1720)	Investors believed a new trading monopoly would unlock unprecedented riches, justifying extreme share prices detached from fundamentals.	Shares rose dramatically then collapsed; widespread losses.	NST historical summary
Roaring Twenties / 1929 Crash (US)	Belief in a “new era” of endless prosperity driven by technology, consumerism, and industrial profits; margin buying and high valuations were rationalized as sustainable. Business Week noted the recurring “new era” illusion.	Market crash; prolonged depression.	FT “New eras, same bubbles”; Irish Times on 1929 parallels
Japanese Asset Bubble (late 1980s)	Decades of strong growth led some to predict Japan would eclipse the US economy under unique structural advantages that rendered prior cyclical risks obsolete.	Real-estate and equity collapse; multi-decade stagnation (“Lost Decades”).	Financial Post on historical peaks
Dot-Com / Internet Bubble (late 1990s–2000)	The internet was said to transform the economy so thoroughly that traditional P/E ratios and profitability no longer applied; companies with little or no revenue received multi-billion valuations.	Nasdaq fell ~78% peak-to-trough; many pure-play firms failed.	NST on Dot-Com narrative; GMO / Templeton reference
US Housing Bubble (mid-2000s)	Widespread conviction that national real-estate prices “could never fall” and that new financial engineering (securitization, subprime lending) had permanently reduced risk.	Housing crash; global financial crisis of 2008.	NST housing example; Reinhart-Rogoff framework
AI / Tech Boom (2020s, ongoing discussion)	Claims that AI is so transformative, or that hyperscaler balance sheets and cash flows make the cycle fundamentally safer than prior tech bubbles, so that elevated valuations and massive CapEx can be sustained under new rules. Parallel debates occur around crypto.	Still unfolding; critics note the classic narrative while supporters emphasize differences in profitability and financing.	Grantham quote coverage; GMO AI analysis

But there are important differences between dot-com financing (venture capital; public equity IPOs of unproven firms; vendor financing) and artificial intelligence financing in the compute infrastructure part of the value chain. 

AI infrastructure spending is dominated by the “Magnificent seven” hyperscalers (Microsoft, Alphabet/Google, Amazon, Meta, and often Nvidia, Apple, Tesla; sometimes Oracle). 

These firms generate enormous free cash flow and operating profits from established, diversified businesses (cloud, advertising, e-commerce, software, chips). 

Early-to-mid phases of the buildout were largely self-funded from internal cash flows and strong margins rather than pure external speculative capital. 

That provides some resilience to credit tightening, which stopped the dot-com bubble in its tracks. 

Many recipients had limited or no profits, weak balance sheets, and business models centered on “eyeballs” or future monetization. When capital markets tightened or growth disappointed, cascading failures ensued; overbuilt fiber and equipment sat underutilized for years.

Venture capital still plays a large role in pure-play AI startups, and there are circular elements (investments, capacity commitments, and vendor-like arrangements involving Nvidia, OpenAI, CoreWeave, etc.). 

Still, the bulk of physical infrastructure investment by hyperscalers has been anchored by operating profits.

Operational (internal cash flow) financing provides more bubble resilience than pure VC financing:

• Hyperscalers can slow spending, absorb write-downs or lower returns on data centers/chips without bankruptcy and continue funding core non-AI businesses. Lower equity valuations, delayed returns, or margin pressure can happen, but there is much less danger of widespread defaults.

• VC funding is less stable. When sentiment shifts, capital can dry up quickly, leading to mass failures of non-viable firms.

• Hybrid/circular financing sits in between: it can inflate activity and create reflexive loops (spending supports valuations that support more financing), but is anchored by solvent, profitable buyers.

That reliance on operating earnings rather than venture capital reduces the probability of a broad credit crunch or mass bankruptcies.

A full dot-com-style multi-year tech bear market with trillions in equity destroyed is less likely precisely because the financing base and profitability differ. 

There are lots of real risks from energy constraints, component costs, regulatory issues, monetization and overbuilding. 

So even if every bubble has some common elements, that does not mean they are identical. Despite the risks, a devastating financing crash on the pattern of the internet bubble seems less likely.

AI Green Shoots from Microsoft, Amazon and Google

Microsoft and Amazon quarterly earnings will not put all concerns about artificial intelligence capital investment to permanent rest. 

But the news is encouraging. Microsoft’s commercial remaining performance obligation (RPO) hit $678 billion in the second quarter, up 84 percent year over year. 

That’s more than double the company’s entire annual revenue of $332 billion. 

Microsoft CFO Amy Hood confirmed that all sequential RPO growth came from customers outside the major AI model makers. 

In other words, regular enterprises are locking in multi-year Azure commitments. 

Google capex continues to worry investors, as do Oracle debt burdens and Meta cash flow. But revenue performance of the sort Microsoft and Amazon are showing, plus growth at Google, suggest high-performance-computing services are generating direct revenue growth. 

Company	Cloud business	Q2 2026 evidence	Why it indicates AI demand
Amazon	AWS	AWS revenue grew 37% YoY to $42.2B, the fastest growth in more than four years. Contract backlog reached $496B. AWS AI and custom-chip businesses each exceeded a $25B annual run rate. (reuters.com)	Management attributed much of the acceleration to generative AI training and inference workloads. Capacity remains constrained despite massive investment.
Microsoft	Azure	Azure revenue increased 43%. Microsoft Cloud reached roughly $59.3B quarterly revenue. Microsoft 365 Copilot surpassed 30 million paid seats. (AP News)	AI workloads are driving Azure consumption while Copilot directly monetizes generative AI software.
Alphabet	Google Cloud	Google Cloud continued exceptionally rapid growth (roughly 60%+), while management repeatedly highlighted AI infrastructure demand and Gemini adoption. (Futurum)	Google now sells both AI infrastructure and AI models, creating two complementary revenue streams.

Also, management teams increasingly argued that AI is now contributing to revenue growth not only in cloud computing but across multiple business lines.

Company	Business segment	Evidence AI contributes
Amazon	Advertising	Advertising revenue increased about 26%. Amazon attributes improvements partly to AI-powered advertising optimization and conversational shopping experiences that improve conversion. (Amazon News)
Amazon	E-commerce	AI-powered product discovery, recommendation systems, inventory forecasting, robotics and delivery optimization contribute to higher retail productivity and better customer experience. Management highlighted record Prime delivery speeds. (AP News)
Amazon	Semiconductor business	Trainium and Graviton chips are now substantial businesses supporting both AWS customers and Amazon's own infrastructure. (MarketWatch)
Microsoft	Microsoft 365	Copilot subscriptions generate entirely new recurring revenue while encouraging premium licensing upgrades. (AP News)
Microsoft	GitHub	GitHub Copilot has become one of Microsoft's fastest-growing developer products, increasing Azure consumption while adding subscription revenue. (The Times of India)
Microsoft	Dynamics & Business Apps	AI assistants increase customer willingness to purchase higher-value enterprise software bundles, although this remains a smaller contributor than Azure. (AP News)
Alphabet	Search	AI Overviews and Gemini improve search engagement while preserving advertising volume. Google continues to report healthy Search revenue despite AI-generated answers. (blog.google)
Alphabet	Advertising	AI improves targeting, campaign optimization and automated creative generation for advertisers. This raises advertising effectiveness rather than replacing advertising. (blog.google)
Alphabet	Workspace	Gemini subscriptions create a growing AI software revenue stream alongside traditional productivity software. (blog.google)

The strongest evidence of AI-driven revenue remains in the cloud businesses, where AWS, Azure, and Google Cloud all reported exceptionally strong growth tied directly to AI workloads. However, the second quarter 2026 results also suggest AI is beginning to enhance the economics of legacy businesses rather than simply creating new standalone AI products.

For Amazon, AI appears to be improving retail operations, logistics, advertising, and semiconductor sales. 

For Microsoft, AI is increasing the value of Microsoft 365, GitHub, and business applications in addition to Azure. 

For Alphabet, AI is strengthening Google Cloud while also supporting Search, advertising, and Workspace.

Though all concerns have not been vanquished, the revenue evidence investors wanted is starting to show up in the financial results, at least from some of the leading hyperscalers making huge investments.

Like Texas, With AI "Everything is Bigger"

In many ways, vendor financing of artificial intelligence infrastructure is a bit like Texas: “everything’s bigger.”

Nobody knows yet whether “circular financing” is going to be a major problem in the artificial intelligence business, but it’s reaching new levels. 

Nvidia, for example, is pondering commitments to OpenAI of about $600 billion, including:

• An OpenAI Ohio data center lease financial guarantee of $250 billion 

• Separately, financing another $350 billion of GPU purchases for OpenAI. 

If completed, that would represent one of the largest examples of vendor-supported infrastructure finance in technology history.

Vendor financing has been provided by companies such as Cisco, Lucent, IBM, and GE Capital in the past, but not at such scale.

But Nvidia has increasingly used several mechanisms to support customers beyond simply shipping chips.

Customer	Approximate size	Nvidia role	Similarity to Ohio deal
OpenAI (Ohio campus)	Project >$500B; reported $250B guarantee plus possible $350B GPU financing	Credit guarantee, GPU financing, hardware supplier	Most extensive
OpenAI (2025 infrastructure agreement)	Up to $100B investment commitment	Infrastructure investment tied to deployment of Nvidia systems	High (Fierce Network)
CoreWeave	Multi-billion-dollar	Equity investor; guaranteed purchases of unused cloud capacity	High (Reuters)
CoreWeave	Multiple equity rounds	Early strategic investor before IPO	Medium (Reuters)
xAI	Tens of billions in GPU systems	Large hardware supplier; strategic ecosystem partner	Moderate (Reuters)
Oracle / Stargate	Hundreds of billions of AI infrastructure	Hardware supplier and infrastructure partner	Moderate (SSRN)
Numerous AI startups	Hundreds of millions to billions	Venture investments through NVentures plus preferred GPU access	Lower, but follows same ecosystem strategy (NVIDIA)

To some extent, Nvidia’s moves are an example of how various contestants in the AI value chain are staking claims in broader roles within the value chain. High-performance computing services suppliers such as Amazon and Google create their own chips and sponsor or create their own language models.

So it might not be surprising to see Nvidia taking on new roles as well. 

Function	Nvidia role
GPU supplier	Sell chips
Systems supplier	Sell complete AI clusters
Platform company	CUDA, networking, software, AI factories
Capital provider	Equity investments, financing, guarantees, demand commitments

The reported Ohio arrangement is not simply a very large chip sale. 

It would make Nvidia part supplier, part infrastructure financier, and part credit guarantor.Nvidia has previously invested in customers such as CoreWeave and OpenAI,  and has used demand guarantees and equity investments to accelerate AI infrastructure.

But such financing has been a staple of the computing industry since the time of mainframes. 

Vendor financing has been a recurring feature of the computing industry for more than 60 years. It tends to emerge during periods when a new generation of computing requires exceptionally large up-front investment. 

The mechanism changes over time, from leases to loans to equity investments to purchase guarantees.

But the economic logic remains consistent: If customers cannot afford the infrastructure needed to create the next wave of demand, suppliers help finance that infrastructure.

The reported Nvidia/OpenAI proposal is best understood as the latest version of this long-running pattern.

Era	Dominant technology	Financing mechanism	Strategic purpose
1960s–1970s	Mainframes	Leasing	Reduce customer capital burden
1980s	Minicomputers	Vendor credit	Expand installed base
1990s	Enterprise networking	Vendor financing	Accelerate Internet buildout
2000s	Telecom & hosting	Vendor loans, export finance	Support infrastructure expansion
2010s	Cloud computing	Long-term purchase commitments	Enable hyperscale investment
2020s	AI infrastructure	Equity, guarantees, GPU financing	Accelerate AI ecosystem growth

AI infrastructure is so capital-intensive that financing has returned to center stage.

Supplier	Customer	Financing approach	Circular element
Nvidia	CoreWeave	Equity investment plus demand guarantees	Nvidia helps create GPU demand
Nvidia	OpenAI	Reported credit guarantees and GPU financing	Financing supports purchases of Nvidia GPUs
AMD	Various AI cloud providers	Strategic investments and joint development (smaller scale)	Encourages accelerator adoption
Microsoft	OpenAI	Multi-billion-dollar investments tied to Azure usage	Investment generates Azure revenue
Amazon	Anthropic	Multi-billion-dollar investment tied to AWS usage	Investment drives AWS consumption
Google	Anthropic	Large investment tied to Google Cloud	Investment increases cloud demand

History suggests such financing can work. But history also suggests it can fail. We still do not know what the AI outcome will be. 

Condition	IBM	Cisco	Nvidia
Technology creates lasting productivity gains	✔	✔	Likely
Customers eventually generate sustainable cash flow	✔	Mixed	Unknown
Vendor does not assume excessive credit risk	✔	No	Still uncertain

Across six decades, the industry has repeatedly followed the same sequence:

• A breakthrough technology emerges (mainframes, PCs, the Internet, cloud, AI)

• Infrastructure costs initially exceed customers' ability or willingness to pay

• Suppliers devise financing mechanisms to accelerate adoption

• If demand proves durable, the financing is remembered as visionary

• If demand disappoints, the same financing is criticized as excessive risk-taking.

The reported Nvidia–OpenAI arrangement is unprecedented in scale, but not in principle. The novelty lies less in the existence of vendor financing than in its magnitude: guarantees and financing measured in the hundreds of billions of dollars rather than millions or even billions.